In quantum computing, achieving error correction comparable to classical systems is crucial for long-term success. This involves measuring and extracting error syndromes to prevent error propagation. However, the cons...
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Skin disorders are a major worldwide health concern that require sophisticated diagnostic technologies for prompt and accurate identification. A deep learning approach that utilizes the DenseNet architecture for derma...
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In this paper, a power cloud platform analysis and computing service system based on Docker and micro-service is proposed by taking advantage of cloud platform storage and computing resources. Based on key technologie...
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With the rise of edge computing in the computing models of today, resource management efficiency is a key aspect, notably CPU utilization. The purpose of this paper is to investigate autoscaling in edge computing. The...
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A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is di...
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When running a panoramic video stitching system on a UAV, he low computing power of the embedded system loaded on the UAV and the changing scenes during the flight cause a large color difference in the panoramic pictu...
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In order to improve the utilization and user experience of large cloud computing center resources in the mobile edge computing (MEC) environment, a dynamic computing resource and spectrum resource allocation algorithm...
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This research introduces a smart helper designed to assist farmers in remote locations. The proposed system provides timely and relevant information on various aspects of farming, including soil management, pest contr...
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Hyperspectral images have a lot of spatial and spectral information, which makes them great for fine crop classification and detection. The hyperspectral remote sensing is more capable than panchromatic remote sensing...
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Machine condition monitoring and predictive maintenance are critical components of smart manufacturing. The realization of machine predictive maintenance relies on synthetic integration of advanced sensing, Internet o...
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ISBN:
(纸本)9798350380903;9798350380910
Machine condition monitoring and predictive maintenance are critical components of smart manufacturing. The realization of machine predictive maintenance relies on synthetic integration of advanced sensing, Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI)-enabled data analytics for machine condition diagnosis and prognosis. One challenge that hurdles the manufacturing shop floor from implementing predictive maintenance is its outdated hardware and limited internet bandwidth, which prevents the floor from transmitting all collected data to the cloud for centralized data analytics. One promising solution is edge-cloud computing and decision-making architecture. In such an infrastructure, collected sensing data will be first processed on edge to directly obtain machine diagnosis results (e.g., normal vs. abnormal), and only extracted features or selected raw data will be transmitted to the cloud for further analytics (e.g., remaining life prognosis). To improve the affordability and applicability of edge computing, this study proposed a novel and deployable edge device that adopts a feature-based Tiny Machine Learning (Tiny ML) model to achieve vibration data processing and machine condition monitoring while maintaining the modeling generalizability and computing efficiency. It integrates a Microelectromechanical Systems (MEMS) accelerometer for vibration data sampling (up to 20 kHz) and a microcontroller for edge computing and communication to the cloud. The integral IoT system, which involves the edge device, cloud services, and wireless communication between the edge and cloud is presented, emphasizing its plug-and-play capability. In this framework, MQTT protocol is adopted for communication among edge, cloud, and end users, allowing processed signal information and machine diagnosis results to be published to end users in real-time. Extensive experiments have been conducted in the context of industrial motor condition monitoring and faul
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